1: Results

Manual Annotation

Cells identified as NA or unknown were not included when calculating the sum of cells from each patient. The ratio of CD4/CD8 is calculate as the number of “CD4 T memory” and “CD4 T regulatory” divided by “CD8 T”.
Wilms2:
Wilms3: 0.54
Wilms1: 0.15

Cell Number
Cell Proportion (%)
Wilms2 Wilms3 Wilms1 Wilms2 Wilms3 Wilms1
Age(month) 8 30 50 NA NA NA
B cell 0 7 28 0.00 0.39 1.55
CD4 T memory 3 97 13 0.17 5.41 0.72
CD4 T regulatory 0 5 8 0.00 0.28 0.44
CD8 T 0 189 140 0.00 10.55 7.76
Dendritic cell 30 85 54 1.69 4.74 2.99
Endothelium 33 191 161 1.86 10.66 8.92
Epithelium 625 235 424 35.21 13.11 23.49
Fibroblast 143 443 230 8.06 24.72 12.74
M2 Macrophage 0 79 21 0.00 4.41 1.16
Mast cell 242 26 1 13.63 1.45 0.06
Monocyte 24 100 240 1.35 5.58 13.30
NK cell 390 138 170 21.97 7.70 9.42
Red blood cell 170 0 12 9.58 0.00 0.66
unknown 115 197 303 6.48 10.99 16.79
sum 1775 1792 1805 100.00 100.00 100.00

SingleR Annotation

Cells identified as NA or unknown were not included when calculating the sum of cells from each patient. The ratio of CD4/CD8 is calculate as the number of “T cells, CD4+” divided by “T cells, CD8+”.
Wilms2: 0.75
Wilms3: 15.54
Wilms1: 3.73

Cell Number
Cell Proportion (%)
Wilms2 Wilms3 Wilms1 Wilms2 Wilms3 Wilms1
Age(month) 8 30 50 8.00 30.00 50.00
B_cell 0 6 30 0.00 0.41 1.92
Endothelial_cells 27 167 148 1.94 11.36 9.49
Epithelial_cells 606 213 272 43.60 14.49 17.44
Macrophage 8 137 57 0.58 9.32 3.65
Monocyte 117 132 305 8.42 8.98 19.55
Neutrophils 2 6 15 0.14 0.41 0.96
NK cells 517 410 352 37.19 27.89 22.56
Pre-B_cell_CD34- 91 21 28 6.55 1.43 1.79
T cells, CD4+ 3 202 183 0.22 13.74 11.73
T cells, CD8+ 4 13 49 0.29 0.88 3.14
Tissue_stem_cells 15 163 121 1.08 11.09 7.76
sum 1390 1470 1560 100.00 100.00 100.00

Annotation from raw article

Cells identified as NA or unknown were not included when calculating the sum of cells from each patient. The ratio of CD4/CD8 is calculate as the number of “T regulatory” and “Th cell” divided by “CD8 T”.
Wilms2: 5.5
Wilms3: 5.93
Wilms1: 0.9

Cell Number
Cell Proportion (%)
Wilms2 Wilms3 Wilms1 Wilms2 Wilms3 Wilms1
Age(month) 8 30 50 8.00 30.00 50.00
B cell 4 6 25 0.29 0.31 1.38
CD8 T cell 2 29 97 0.15 1.48 5.34
Endothelium 30 160 134 2.19 8.15 7.38
Epithelium 544 742 530 39.71 37.82 29.19
Erythroblast 110 0 0 8.03 0.00 0.00
Fibroblast 2 51 14 0.15 2.60 0.77
Mast cell 16 25 0 1.17 1.27 0.00
Mononuclear phagocyte 84 105 220 6.13 5.35 12.11
Myeloid 25 10 11 1.82 0.51 0.61
Nephron_epithelium 2 28 22 0.15 1.43 1.21
Neutrophil 3 33 19 0.22 1.68 1.05
NK cell 374 54 156 27.30 2.75 8.59
NKT cell 8 117 25 0.58 5.96 1.38
Normal_cell 0 1 5 0.00 0.05 0.28
others 107 140 22 7.81 7.14 1.21
Plasma cell 0 1 4 0.00 0.05 0.22
Plasmacytoid DC 26 57 95 1.90 2.91 5.23
Private 6 101 33 0.44 5.15 1.82
Proliferating NK cell 1 0 1 0.07 0.00 0.06
Proliferating T cell 1 2 0 0.07 0.10 0.00
T regulatory 0 1 2 0.00 0.05 0.11
Th cell 11 171 85 0.80 8.72 4.68
Tumor 14 128 316 1.02 6.52 17.40
sum 1370 1962 1816 100.00 100.00 100.00

2: Annotation of Wilms Dataset

There were 72501 cells passing the quality control in the article, and 11407 of them were sampled from wilms patients. It will be included in this analysis if the cell passed the QC and were from wilms patients.

The first round of annotation followed the pipeline as “clustering –> finding differentially expressed gene –> manual annotation”. As the author instructed, the batch effect between different 10X channels was removed via “harmony”.

UMAP plot with batch effect removed
distribution of canonical marker
unsupervised clustering (resolution = 0.4, npc = 15)
differentially expressed genes in each seurat_cluster
Annotation from the author

3: Annotation of T cell in Wilms Dataset

Manual Annotation

UMAP plot with batch effect removed
distribution of canonical marker
unsupervised clustering (resolution = 1.2, npc = 30)
differentially expressed genes in each seurat_cluster

SingleR Annotation

Annotation from raw article

4: Annotation of Myeloid cell

Manual Annotation

UMAP plot with batch effect removed
distribution of canonical marker
unsupervised clustering (resolution = 1.2, npc = 30)
differentially expressed genes in each seurat_cluster

SingleR Annotation

Annotation from raw article